Information Arrivals, Stock Price Variability and Market Efficiency in Indian Stock Market

1996 ◽  
Vol 38 (1) ◽  
pp. 20
Author(s):  
S. Amanulla ◽  
Arun Kumar Giri ◽  
B. Kamaiah
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Stock Price ◽  
Price Variability ◽  
Indian Stock Market

scholarly journals Market Efficiency and Integration: An Examination of Indian Stock Market

2003 ◽  
Author(s):  
Chandra Shekhar Bhatnagar
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Stock Price ◽  
Price Index ◽  
Weak Sense ◽  
Weak Form ◽  
Strong Form ◽  
Emerging Stock Markets ◽  
Stock Price Index ◽  
Indian Stock Market

This paper examines the efficiency and integration of the Indian stock market. The weak form of efficiency has been tested by studying the stationarity characteristics of theMSCI Stock Price Index of India. For testing the semi-strong form of efficiency and integration of the Indian Stock Market with the macro phenomenon of emerging stock markets of the world, the causality between the MSCI Stock Price Index of India and the MSCI EMF Index has been studied. The results point out that the Indian Stock Market is efficient in its weak sense. However, the same is not true for the semi-strong form of market efficiency. Therefore, the utility of a forecasting model having the macro phenomenon (MSCI EMF Index in the present case) as a forecasting variable cannot be ruled out.  

Long-term reactions to large stock price declines and increases in the European stock market: a note on market efficiency

2010 ◽  
Vol 36 (2) ◽  
pp. 400-423 ◽  
Author(s):  
Achim Himmelmann ◽  
Dirk Schiereck ◽  
Marc W. Simpson ◽  
Moritz Zschoche
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Stock Price

Institutional Herding: Causality and Persistence

2021 ◽  
pp. 227797522110402
Author(s):  
S S S Kumar
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Mutual Funds ◽  
Institutional Investors ◽  
International Markets ◽  
Jel Classification ◽  
Trading Strategies ◽  
Trading Activity ◽  
Indian Stock Market ◽  
Institutional Herding

We investigate the causality in herding between foreign portfolio investors (FPIs) and domestic mutual funds (MFs) in the Indian stock market. The estimated herding levels are considerably higher than those observed in other international markets, and herding is prevalent in small stocks. We find that institutional investors follow contrarian-trading strategies, unlike what was documented in most other markets. Analysis of the aggregate herding measure shows a bi-directional causality between FPIs and MFs. Further analysis using directional herding measures indicate no evidence of causality between institutional herds on the sell-side. But we find causality on the buy-side and it is running in both directions between FPIs and MFs, implying a feedback of information. Given the tendency of institutions for herding in small stocks, adopting contrarian-trading strategies, the observed sell-side causality is perhaps having a salubrious effect. As institutional investors are contrarians, their trading activity will lead to price corrections in small stocks aligning with the fundamentals, thereby contributing to market efficiency. JEL Classification: C23, C58, G23, G15, G40

scholarly journals O SETOR DE CONSUMO CÍCLICO SEGUNDO AÇÕES NEGOCIADAS NA B3: O SEGMENTO DE ELETRODOMÉSTICOS, PRODUTOS DIVERSOS,TECIDOS,VESTUÁRIOE CALÇADOS

2020 ◽  
Vol 3 (4) ◽  
pp. 37-46
Author(s):  
Rafael Gutierres Castanha ◽  
Andreia de Fatima Costa Miranda ◽  
Lucas Alves de Pontes
Keyword(s):  
Stock Market ◽  
Correlation Coefficient ◽  
Financial Market ◽  
Stock Price ◽  
Stock Exchange ◽  
Methodological Approach ◽  
Price Variability ◽  
Pearson’S Correlation Coefficient ◽  
Home Appliances ◽  
The Stability

By analyzing a portion of the Brazilian financial market, according to the daily value of its shares traded on the largest stock exchange in the country, the B3 stock exchange, offering possibilities to understand more clearly the behavior of the stock market according to growth, decrease, and even the stability of the values traded on the stock market in question. Thus, this research presents an analysis using Pearson's correlation coefficient and offers elements to affirm or refute the idea of proximity between companies in the same sector or not. By proposing the application of this methodology in the segment of home appliances, miscellaneous products, and fabrics, clothing and footwear, it is possible to point out how closely these companies are interconnected in terms of stock price variability. Thus, the objective was to observe not only the behavior of the stock price of the companies of the sector in question during a given period, as well as the intensity of variation between the same measures by the correlation coefficient, but also to evaluate the use of this coefficient as a proposal. methodological approach to assess the proximity between the companies. As a result, it was concluded that the largest proximityis between companies of the same segment.

scholarly journals Prediction of Option Price using Ensemble of Machine Learning Algorithms for Indian Stock Market

2019 ◽  
Vol 8 (2) ◽  
pp. 3231-3241
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Learning Algorithm ◽  
Option Price ◽  
Machine Learning Algorithms ◽  
Parametric Models ◽  
Accuracy Of Prediction ◽  
Indian Stock Market

The non-deterministic behavior of stock market creates ambiguities for buyers. The situation of ambiguities always finds the loss of user financial assets. The variations of price make a very difficult task to predict the option price. For the prediction of option used various non-parametric models such as artificial neural network, machine learning, and deep neural network. The accuracy of prediction is always a challenging task of for individual model and hybrid model. The variation gap of hypothesis value and predicted value reflects the nature of stock market. In this paper use the bagging method of machine learning for the prediction of option price. The bagging process merge different machine learning algorithm and reduce the variation gap of stock price.

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